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Energy transition to lower EROI sources (v1.0)

Josh Floyd
This is the original model version (v1.0) with default "standard run" parameter set: see detailed commentary here and here. As of 2 September 2015, ongoing development has now shifted to this version of the model.

The significance of reduced energy return on energy invested (EROI) in the transition from fossil fuel to renewable primary energy sources is often disputed by both renewable energy proponents and mainstream economists.​ This model illustrates the impact of EROI in large-scale energy transition using a system dynamics approach. The variables of primary interest here are: 1) net energy available to "the rest of the economy" as renewable penetration increases [Total final energy services out to the economy]; and 2) the size of the energy sector as a proportion of overall economic activity, treating energy use as a very rough proxy for size [Energy services ratio].
This model aggregates energy supply in the form of fuels and electricity as a single variable, total final energy services, and treats the global economy as a single closed system.
The model includes all major incumbent energy sources, and assumes a transition to wind, PV, hydro and nuclear generated electricity, plus biomass electricity and fuels. Hydro, biomass and nuclear growth rates are built into the model from the outset, and wind and PV emplacement rates respond to the built-in retirement rates for fossil energy sources, by attempting to make up the difference between the historical maximum total energy services out to the global economy, and the current total energy services out. Intermittency of PV and wind are compensated via Li-ion battery storage. Note, however, that seasonal variation of PV is not fully addressed i.e. PV is modeled using annual and global average parameters. For this to have anything close to real world validity, this would require that all PV capacity is located in highly favourable locations in terms of annual average insolation, and that energy is distributed from these regions to points of end use. The necessary distribution infrastructure is not included in the model at this stage.
It is possible to explore the effect of seasonal variation with PV assumed to be distributed more widely by de-rating capacity factor and increasing the autonomy period for storage.

This version of the model takes values for emplaced capacities of conventional sources (i.e. all energy sources except wind and PV) as exogenous inputs, based on data generated from earlier endogenously-generated emplaced capacities (for which emplacement rates as a proportion of existing installed capacity were the primary exogenous input).

Energy EROI Economy

  • 1 year 11 months ago

Energy transition to lower EROI sources (v2.5)

Josh Floyd
A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

Energy EROI Economy

  • 1 year 5 months ago

Energy transition to lower EROI sources (v2.6)

Josh Floyd
A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 29 June 2016 (v2.6): Added historical emplacement for wind and PV capacity. The maximum historical emplacement rates are then maintained from year 114/115 until the end of the model period. This acts as a base emplacement rate that is then augmented with the contribution made via the feedback control mechanism. Note that battery buffering commences only once the additional emplacement via the feedback controller kicks in. This means that there is a base capacity for both wind and PV for which no buffering is provided, slightly reducing the energy services required for wind and PV supplies, as well as associated costs. Contributions from biomass and nuclear have also been increased slightly, in line with the earlier intention that these should approximately double during the transition period. This leads to a modest reduction in the contributions required from wind and PV.

Added calculation of global mean conversion efficiency energy to services on primary energy basis. This involves making a compensation to the gross energy outputs for all thermal electricity generation sources. The reason for this is that standard EROI analysis methodology involves inclusion of energy inputs on a primary energy equivalent basis. In order to convert correctly between energy inputs and energy service inputs, the reference conversion efficiency must therefore be defined on a primary energy basis. Previously, this conversion was made on the basis of the mean conversion efficiency from final energy to energy services.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

Energy EROI Economy

  • 1 year 1 month ago

Energy and Economic Activity

Hanns-Jürgen Hodann
The statement that there can be no economic activity without  energy and that fossil fuels are finite contrasts with the fact that money is not finite and can be created by governments via their central banks at zero marginal cost whenever needed.

An important fact about COAL, GAS and OIL (even when produced via fracking) is that their net energy ratios are falling rapidly. In other words the energy needed to extract a given quantity of fossil fuels is constantly increasing. This ratio (Energy Invested on Energy Returned - EIOER) provides yet another warning that we can no longer rely on fossil fuels to power our economies. We cannot wait until the ratio falls to 1/1 before we invest seriously in alternative sources of energy, because by then industrial society as we know it doday will have ceased to exist. 

PS: A link between growth in energy consumption and GDP growth is clearly illustrated on slide 13 of Gail Tverberg's presentaion entitled ''Oops! The world economy depends on an energy-related bubble''. In fact, the slide shows that growth in energy consumption usually precedes GDP growth.

https://gailtheactuary.files.wordpress.com/2015/10/oops-debt-bubble-10_30_15.pdf

Energy Economy Fracking

  • 1 year 8 months ago

Energy transition to lower EROI sources (v3.0)

Josh Floyd
Major update 12 December 2015 (v3.0): This new version of the model overhauls the way that incumbent energy source (fossil sources plus biomass, hydro electricity and nuclear electricity) supply capacity is implemented. This is now based on direct (exogenous) input of historical data, with the future supply curve also set directly (but using a separate input array to the historical data). For coal and natural gas fired electricity, this also requires that the simple, direct-input EROI method be used (i.e. same as for coal and NG heating, and petroleum transport fuels).

Note that this new version of the model no longer provides a historical view of the emplacement rates for energy supply sources other than wind and PV, and therefore no longer allows comparison of required emplacement rates for wind and PV with incumbent energy sources. Output data relating to this is available in model version v2.5 (see link below), for the specific transition duration built into that version of the model.

The previous version of the model (version 2.5) is available here.

The original "standard run" version of the model (v1.0) is available here.

Energy EROI Economy

  • 1 year 6 months ago

Energy transition to lower EROI sources (v3.1)

Josh Floyd
Update 24 Feburary 2016 (v3.1): This version has biomass, hydro and nuclear continuing at pre-transition maxima, rather than increasing. The combined emplacement rate cap for wind and PV is set at a default value of 5000 GW/year.

Major update 12 December 2015 (v3.0): This new version of the model overhauls the way that incumbent energy source (fossil sources plus biomass, hydro electricity and nuclear electricity) supply capacity is implemented. This is now based on direct (exogenous) input of historical data, with the future supply curve also set directly (but using a separate input array to the historical data). For coal and natural gas fired electricity, this also requires that the simple, direct-input EROI method be used (i.e. same as for coal and NG heating, and petroleum transport fuels).

Note that this new version of the model no longer provides a historical view of the emplacement rates for energy supply sources other than wind and PV, and therefore no longer allows comparison of required emplacement rates for wind and PV with incumbent energy sources. Output data relating to this is available in model version v2.5 (see link below), for the specific transition duration built into that version of the model.

The previous version of the model (version 2.5) is available here.

The original "standard run" version of the model (v1.0) is available here.

Energy EROI Economy

  • 1 year 5 months ago

LIMITS TO ECONOMIC GROWTH AND PROMINENT NEGATIVE FEEDBACK LOOPS

Hanns-Jürgen Hodann
To maintain economic wealth (roads, hospitals, power lines, etc.) power needs to be consumed. The same applies to economic activity, since any activity requires the consumption of energy. According to the Environmental Protection Agency, the burning of fossil fuels was responsible for 79 percent of U.S. greenhouse gas emissions in 2010. So whilst economic activity takes place fossil fuels will be burned and CO2 emissions are unavoidable - unless we use exclusively renewable energy resources, which is not likely to occur very soon. However, the increasing CO2 concentrations in the atmosphere will have negative consequences, such droughts, floods, crop failures, etc. These effects represent limits to economic growth. The CLD illustrates some of the more prominent negative feedback loops that act as a break on economic growth and wealth.  As the negative feedback loops (B1-B4) get stronger, an interesting question is, 'will a sharp reduction in economic wealth and unavoidable recession lead to wide-spread food riots and disturbances?'

Limits To Growth Energy Economy Global Warming

  • 8 months 1 week ago

Microeconomic Savings can convert to Macroeconomic Costs

Hanns-Jürgen Hodann
Microeconomic measures can produce counterintuitive 'emergent' effects at the macro or systemic level. The commendable act of saving money by individuals during uncertain economic times has the perverse macroeconomic effect of making a recession  worse: in aggregate there will be less money available for spending, suppressing demand for goods and services. Economists call this effect 'the paradox of thrift'. Similarly, logical efforts by companies in such conditions to reduce their wage bill or their postponement of investment decisions will reduce spending in the economy  and deepen the economic downturn.

What can be done to counteract this harmful dynamic? The missing spending can be replaced by government spending: governments have it within their power to effectively counter economic downturns!

Economy Emergent Phenomena Fiscal Spending Paradox Of Thrift

  • 1 week 4 days ago

ROBOTS AND A DISATROUS ECONOMIC DYNAMIC

Hanns-Jürgen Hodann
​In a recent report, the World Economic Forum considered that the use of robots in economic activity will cause far more job losses in the near future than there will be new ones created. Every economic sector will be affected. The CLD tries to illustrate the dynamic effects of replacing human workers with robots. This  dynamic  indicates that if there is no replacement of the  income forgone by the laid off workers, then the economy will soon grind to a halt. To avoid disaster, there must be enough money in circulation, not parked in off-shore investments, to permit the purchase of all the goods and services produced by robots. The challenge for the government is to make sure that this is  case.  

Robots Economy Social Employment

  • 8 months 3 days ago

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